Recent studies have demonstrated that data-driven AMC can achieve superior classification performances compared to the model-driven schemes by learning effective representations from data [1]}, [2]}, [3]}. Machine learning (ML) based schemes such as support vector machine (SVM), K-nearest neighbor (KNN), and logistic regression can recognize the modulation formats by using a large number of data from the received signals. However, ML-based models still rely on the features generated by FB-based schemes. Thus, deep learning (DL) based algorithms [1]}, [2]}, [3]} were proposed to extract features automatically from the original data, such as I/Q samples, cyclic spectrum, constellation diagrams.
